Systems and methods for determining a heart rate of an imaged heart in an ultrasound image feed

ABSTRACT

The present embodiments relate generally to systems, methods, and apparatus for determining a heart rate of an imaged heart in an ultrasound image feed. A plurality of ultrasound images can first be acquired. For each ultrasound image of the plurality of ultrasound images, the image can be divided into a plurality of regions; where each region is positioned so that the region corresponds to a substantially similar region location present across the plurality of ultrasound images. For each region location on each of the plurality of ultrasound images, a statistical calculation on image data of the region location can be performed. For each region location, a frequency of change in the statistical calculation over the plurality of ultrasound images can then be determined. The region location having a dominant determined frequency of change in the statistical calculation (over the plurality of ultrasound images) can be identified as a region of interest (ROI). The heart rate can be calculated based on the determined frequency of change of the ROI.

FIELD

The present disclosure relates generally to ultrasound imaging, and inparticular, systems and methods for determining a heart rate of animaged heart in an ultrasound image feed.

BACKGROUND

Ultrasound imaging systems are a powerful tool for performing real-timeimaging procedures in a wide range of medical applications. Whenperforming standard imaging, ultrasound systems are typically operatedin brightness-mode (commonly known as B-mode). When using B-mode toperform cardiac imaging, the motion of a heart beating can be viewed.

Traditionally, to measure the heart rate of an imaged heart, anultrasound operator may select the system to be operated in motion-mode(commonly called M-mode). In M-mode, a single scan line is placed alongan area of interest, and imaging along that one scan line issuccessively displayed along the X-axis over time. When there is motionalong the scan line (e.g., when the scan line is placed so that ittraverses the wall of a heart as it moves towards and away from theprobe head), the resultant M-mode image appears as if it has a waveformthat reflects the motion over time. A peak-to-peak measurement of thiswaveform can provide a heart rate measurement (after the sampling rateof the ultrasound image feed is taken into account).

M-mode may be cumbersome to use. For example, selecting the initial scanline and/or measuring the peak-to-peak distance may take time andexpertise. As a result, there have been attempts to automate heart ratedetermination during B-mode imaging. These attempts typically involveautomating manual M-mode processes. For example, in one scenario, a scanline is automatically selected and a time-series is generated over aseries of frames at this scan line. A spectral analysis (such as aFourier transform) is then performed on the data in the time-series toidentify a frequency that may be reflective of the heart rate. Sincethis method relies on automated selection of a scan line, it may fail tocapture motion in heart anatomical structures on areas of the image thatdo not intersect the scan line.

In another example, spatial points are identified on an image, and imagedata at these spatial points are plotted with respect to time. A Fouriertransform may then be performed on the plotted image data to identifythe heart rate. Since this method performs Fourier transform on theimage data itself, it is similar to M-mode analyses and relies onplotted image data having a waveform appearance for the Fouriertransform to accurately identify a frequency. However, such methods maynot be sufficiently robust to determine a heart rate in situations wherethe image data at a selected spatial point plotted over time does notproduce a waveform appearance (e.g., where a heart valve is present on aspatial point when it is closed, but not present on that spatial pointwhen the heart valve is open, so that the resultant plotted image dataover time does not produce a waveform).

There is thus a need for improved systems for determining a heart rateof an imaged heart in an ultrasound image feed. The embodimentsdiscussed herein may address and/or ameliorate at least some of theaforementioned drawbacks identified above. The foregoing examples of therelated art and limitations related thereto are intended to beillustrative and not exclusive. Other limitations of the related artwill become apparent to those of skill in the art upon a reading of thespecification and a study of the drawings herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of various embodiments of the present disclosurewill next be described in relation to the drawings, in which:

FIGS. 1A-1D are selected frames of an example ultrasound image feedcontaining an imaged heart, in accordance with at least one embodimentof the present invention;

FIG. 2 is a flowchart for a method of determining a heart rate of animaged heart in an ultrasound image feed, in accordance with at leastone embodiment of the present invention;

FIG. 3 is an illustration of regions that may be positioned on frames ofan ultrasound image feed having an imaged heart, in accordance with atleast one embodiment of the present invention;

FIG. 4 are charts showing plots of a statistical calculation for eachregion shown in FIG. 3, over a number of frames of an ultrasound imagefeed, in accordance with at least one embodiment of the presentinvention;

FIG. 5 is a chart showing a plot of a statistical calculation and acorresponding filtered plot, for a given region shown in FIG. 3, andover a number of frames of an ultrasound image feed, in accordance withat least one embodiment of the present invention;

FIG. 6 are charts showing filtered plots of the statistical calculationsshown in FIG. 4, for each region shown in FIG. 3, in accordance with atleast one embodiment of the present invention;

FIG. 7 are charts showing plots of frequencies present on the filteredplots of FIG. 6, for each region shown in FIG. 3, in accordance with atleast one embodiment of the present invention;

FIG. 8 shows a functional block diagram of an ultrasound system, inaccordance with at least one embodiment of the present invention; and

FIG. 9 is a flowchart for a method of determining a heart rate of animaged in an ultrasound image feed, in accordance with at least oneembodiment of the present invention.

DETAILED DESCRIPTION

In a first broad aspect of the present disclosure, there is provided amethod of determining a heart rate of an imaged heart in an ultrasoundimage feed. The method may involve: acquiring a plurality of ultrasoundimages; for each ultrasound image of the plurality of ultrasound images,dividing the image into a plurality of regions, wherein each region ispositioned so that the region corresponds to a substantially similarregion location present across the plurality of ultrasound images; foreach region location on each of the plurality of ultrasound images,performing a statistical calculation on image data of the regionlocation; for each region location, determining a frequency of change inthe statistical calculation over the plurality of ultrasound images;determining the region location having a dominant determined frequencyof change in the statistical calculation over the plurality ofultrasound images; identifying a selected region location having thedominant determined frequency as a region of interest (ROI); andcalculating the heart rate based on the determined frequency of changeof the ROI.

In some embodiments, the statistical calculation includes an indicationof variation in brightness of the image data of the region location. Insome embodiments, the statistical calculation includes a standarddeviation of brightness values of the image data of the region location.

In some embodiments, when determining the frequency of change for eachregion location over the plurality of ultrasound images, the methodfurther includes: performing a Fourier transform on the statisticalcalculations of the region location across the plurality of ultrasoundimages, to generate a power spectrum; and selecting a peak frequencyfrom the power spectrum as the frequency of change for the regionlocation.

In some embodiments, when determining the region location having thedominant determined frequency of change in the statistical calculation,the method further includes: selecting the region location, of theplurality of regions, having the highest magnitude for the frequency ofchange.

In some embodiments, the method further includes: prior to the dividing,scaling down at least one ultrasound image of the plurality ofultrasound images.

In some embodiments, the plurality of regions forms a grid. In someembodiments, a total number of region locations is less than fifty (50).In some embodiments, a total number of region locations is at least two(2).

In some embodiments, the method further includes performing a band passfilter on at least one determined frequency of a region location toeliminate non-heart-rate frequencies.

In some embodiments, the calculating the heart rate includes:translating the dominant determined frequency of change from a per-framebasis to a per-unit-time basis, and wherein the translating is performedbased on a frame rate of the ultrasound image feed.

In another broad aspect of the present disclosure, there is provided anultrasound imaging apparatus for determining a heart rate of an imagedheart in an ultrasound image feed, the apparatus including a processor,and a memory storing instructions for execution by the processor. Whenthe instructions are executed by the processor. the processor can beconfigured to: acquire a plurality of ultrasound images; for eachultrasound image of the plurality of ultrasound images, divide the imageinto a plurality of regions, wherein each region is positioned so thatthe region corresponds to a substantially similar region locationpresent across the plurality of ultrasound images; for each regionlocation on each of the plurality of ultrasound images, perform astatistical calculation on image data of the region location; for eachregion location, determine a frequency of change in the statisticalcalculation over the plurality of ultrasound images; determine theregion location having a dominant determined frequency of change in thestatistical calculation over the plurality of ultrasound images;identify a selected region location having the dominant determinedfrequency as a region of interest (ROI); and calculate the heart ratebased on the determined frequency of change of the ROI.

In some embodiments, the statistical calculation includes a standarddeviation of brightness values of the image data of the region location.

In some embodiments, when determining the frequency of change for eachregion location over the plurality of ultrasound images, the processoris further configured to: perform a Fourier transform on the statisticalcalculations of the region location across the plurality of ultrasoundimages, to generate a power spectrum; and select a peak frequency fromthe power spectrum as the frequency of change for the region location.

In some embodiments, when determining the region location having thedominant determined frequency of change in the statistical calculation,the processor is further configured to: select the region location, ofthe plurality of regions, having the highest magnitude for the frequencyof change.

In some embodiments, the processor is further configured to: prior tothe dividing, scale down at least one ultrasound image of the pluralityof ultrasound images.

In some embodiments, the processor is further configured to: perform aband pass filter on at least one determined frequency of a regionlocation to eliminate non-heart-rate frequencies.

In another broad aspect of the present disclosure, there is provided acomputer readable medium storing instructions for determining a heartrate of an imaged heart in an ultrasound image feed, the instructionsfor execution by a processor of a computing device. When theinstructions are executed by the processor, the processor is configuredto: acquire a plurality of ultrasound images; for each ultrasound imageof the plurality of ultrasound images, divide the image into a pluralityof regions, wherein each region is positioned so that the regioncorresponds to a substantially similar region location present acrossthe plurality of ultrasound images; for each region location on each ofthe plurality of ultrasound images, perform a statistical calculation onimage data of the region location; for each region location, determine afrequency of change in the statistical calculation over the plurality ofultrasound images; determine the region location having a dominantdetermined frequency of change in the statistical calculation over theplurality of ultrasound images; identify a selected region locationhaving the dominant determined frequency as a region of interest (ROI);and calculate the heart rate based on the determined frequency of changeof the ROI.

In some embodiments, the statistical calculation includes a standarddeviation of brightness values of the image data of the region location.

In some embodiments, when determining the frequency of change for eachregion location over the plurality of ultrasound images, theinstructions further configure the processor to: perform a Fouriertransform on the statistical calculations of the region location acrossthe plurality of ultrasound images, to generate a power spectrum; andselect a peak frequency from the power spectrum as the frequency ofchange for the region location.

In another broad aspect of the present disclosure, there is providedanother method of determining a heart rate of an imaged heart in anultrasound image feed. The method may involve: acquiring a plurality ofultrasound images; calculating a difference between at least twosuccessive images in the plurality of ultrasound images to determinepixel displacement of one or more pixel locations; optionally,performing a morphological operation to locate a region of interest(ROI) in the images where there is periodic motion; for the located ROI,performing a statistical calculation on image data of the regionlocation over the plurality of ultrasound images; determining afrequency of change in the statistical calculation at the ROI over theplurality of ultrasound images; determining the dominant determinedfrequency of change in the statistical calculation at the ROI over theplurality of ultrasound images; and calculating the heart rate based onthe determined dominant frequency of change at the ROI.

For simplicity and clarity of illustration, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements or steps. In addition,numerous specific details are set forth in order to provide a thoroughunderstanding of the exemplary embodiments described herein. However, itwill be understood by those of ordinary skill in the art that theembodiments described herein may be practiced without these specificdetails. In other instances, certain steps, signals, protocols,software, hardware, networking infrastructure, circuits, structures,techniques, well-known methods, procedures and components have not beendescribed or shown in detail in order not to obscure the embodimentsgenerally described herein.

Furthermore, this description is not to be considered as limiting thescope of the embodiments described herein in any way. It should beunderstood that the detailed description, while indicating specificembodiments, are given by way of illustration only, since variouschanges and modifications within the scope of the disclosure will becomeapparent to those skilled in the art from this detailed description.Accordingly, the specification and drawings are to be regarded in anillustrative, rather than a restrictive, sense.

Referring to FIGS. 1A-1D, shown there generally as 100 a-100 d areselected frames of an ultrasound image feed containing an imaged heart,in accordance with at least one embodiment of the present invention. Thefour frames 100 a-100 d are at different successive points in time overthe series of frames. At the first point in time shown in FIG. 1A, aheart valve 102 a can be seen in its closed position. As a cardiac cycleprogresses, the heart valve would proceed to an open position, so thatit would not be viewable in the same image location 102 b at thesubsequent point in time shown in FIG. 1B. As the cardiac cyclecontinues to progress, FIG. 1C and FIG. 1D show the heart valve 102 c,102 d being restored to its closed position. Similarly, the heart wall104 a can be seen in FIG. 1A at the first point in time. Over the courseof successive points in time shown in FIGS. 1B-1D, the heart wallpositions 104 b, 104 c, 104 d can be seen as relaxing and contractingduring diastole and systole phases of the cardiac cycle. The exampleframes shown in FIGS. 1A-1D will be discussed below in the context ofthe methods described herein. For ease of illustration and discussion,the frames shown in FIG. 1A-1D are rotated ‘90’ degrees from theorientation of their capture. However, it will be understood that theprinciples discussed herein may be applicable regardless of theorientation of the ultrasound image frames.

Referring to FIG. 2, shown there generally as 200 is a flowchart for amethod of determining a heart rate of an imaged heart in an ultrasoundimage feed, in accordance with at least one embodiment of the presentinvention. In some embodiments, the various acts shown in FIG. 2 may beperformed in real-time by an ultrasound system or machine (e.g., afunctional block diagram of which is shown in FIG. 8). In someembodiments, the method of FIG. 2 may be performed on image frames of astored cineloop containing video of an imaged heart. In suchembodiments, portions of the cineloop may be considered an ultrasoundimage feed herein.

In discussing the method of FIG. 2 below, reference will simultaneouslybe made to the image shown in FIG. 3, and the data plots shown in FIGS.4-7.

At 205, the method involves acquiring a plurality of ultrasound images.For example, the ultrasound images may be acquired by transmitting andreceiving ultrasound signals into tissue. As a person skilled in the artwould understand, ultrasound image frames may be generated from theseultrasound signals. In various embodiments, a number of these images maybe acquired and stored in memory so that the process of method of FIG. 2may be performed on the stored images. In embodiments where the methodof FIG. 2 is performed in real-time, a circular buffer may be used tostore a set number of images that get replaced as new images areacquired. The method of FIG. 2 may then be continuously performed on theimages in this buffer to provide a real-time determination of heart rateduring imaging. The number of images stored in this buffer can beselected so that the sampling duration is long enough to capturemultiple cardiac cycles. For example, adult humans may potentially haveheart rates as low as ‘30’ beats per minute (bpm) (e.g., a cardiac cycleevery ‘2’ seconds). To ensure there are enough frames in the buffer fordetermining the heart rate when performing the method of FIG. 2, thebuffer may be selected to store frames for at least ‘4’ seconds (e.g.,at least ‘2’ cardiac cycles for an adult human with a ‘30’ bpm heartrate). The number of frames in the buffer that allows for theappropriate sampling duration may vary with the sampling rate. Forexample, if an ultrasound image feed is being acquired at a frame rateof ‘30’ frames per second (fps), then ‘120’ frames would be needed for asampling duration of ‘4’ seconds (‘30’ fps×‘4’ seconds). To provide moredata for the analysis performed in subsequent acts and to enhanceaccuracy in the determined heart rate, the buffer may be selected tohave more than ‘4’ seconds of data. For example, in an exampleembodiment selected to have ‘6’ seconds of data and where the samplingrate is ‘30’ fps, the buffer may have ‘180’ frames (‘30’ fps×‘6’seconds).

Referring still to act 205 of FIG. 2, in embodiments where the method ofFIG. 2 is being performed on a cineloop (e.g., instead of on real-timeultrasound images being acquired live), act 205 may involve reading theultrasound image frames from the cineloop.

At 210 of FIG. 2, the method involves dividing each ultrasound image ofthe plurality of ultrasound images into a plurality of regions. Whenperforming this dividing, each region can be positioned so that theregion corresponds to a substantially similar region location presentacross the plurality of ultrasound images acquired at act 205.

Referring simultaneously to FIG. 3, shown there generally as 300 is anillustration of regions that may be positioned on frames of the exampleultrasound image feed shown in FIGS. 1A-1D, in accordance with at leastone embodiment of the present invention. The regions 302 may form anumber of rows 304 and a number of columns 306 covering an ultrasoundimage (e.g., as illustrated, a ‘3’ row by ‘5’ column grid). This regionconfiguration may be suitable for the example ultrasound image feed ofFIGS. 1A-1D because the imaged heart there is an adult image heart.However, in various embodiments, the grid configuration may be modified.For example, to capture the smaller movements of a fetal heart, a finer(e.g., less coarse, and more granular) grid configuration may bepossible (e.g., ‘4’ rows by ‘10’ columns).

Since increasing the number of total regions 302 may increase the numberof locations on which subsequent acts are to be performed (andcorrespondingly, the processing requirements to perform embodiments ofthe present methods), various grid configurations may be used to balancebetween performance and heart rate accuracy. For example, if the presentmethods were configured to execute in an environment with limitedcomputing resources and/or where it was known that relatively largephysical cardiac anatomical structures were being imaged (e.g., certainveterinarian applications or adult human hearts), a total number ofregion locations may be configured to be as few as ‘2’ (e.g., a ‘2×1’ ora ‘1×2’ grid). In another example, if the present methods were beingconfigured to execute in an environment where there are fewerrestrictions on computing resources and/or if there it was possible thatsmall (e.g., fetal) cardiac structures were being imaged, the totalnumber of region locations can be as many as ‘50’ (e.g., a ‘5×10’ or‘10×5’ grid).

Referring back to FIG. 2, at 215, for each region location on each ofthe plurality of ultrasound images, a statistical calculation on imagedata of the region location may be performed. The statisticalcalculation may be any suitable statistical parameters that providesinformation about the image data in a region location. For example, thestatistical calculation may be the mean brightness value of the pixelswithin a given region. In various embodiments, the statisticalcalculation may include an indication of variation in brightness of theimage data of the region location. For example, in some embodiments, thestatistical calculation may be the variance or standard deviation ofbrightness values of the image data of the region location.

Referring simultaneously to FIG. 4, shown there generally as 40 arecharts showing plots of a statistical calculation for each region shownin FIG. 3, over a number of frames of the example ultrasound image feed,in accordance with at least one embodiment of the present invention. TheX-axis in the plots of FIG. 4 correspond to frame numbers. The plotsshown in FIG. 4 are provided in a ‘3’ row by ‘5’ column configurationsimilar to FIG. 3, where the row by column position of a plot in FIG. 4corresponds to the same row by column region location in the gridconfiguration shown in FIG. 3. The plots 40 are for the examplevideo/series of frames from which the screenshots of FIGS. 1A-1D wereselected.

In FIG. 4, plot 400 (labeled as ‘ROI: 0’) shows the calculated standarddeviation values of an upper left hand corner region 302 over the seriesof frames in the example sequence. As shown, it can be seen there isrelatively little variation in the standard deviation values over theseries of frames. Referring simultaneously back to FIGS. 1A-1D, this isreflected in the top left corner of the screen captures 100 a-100 dbeing primarily black, without much variation in the brightness valuesover the course a cardiac cycle.

Referring still to FIG. 4, plot 401 (labeled as ‘ROI: 1’) shows morevariation in the calculated standard deviation over the course of theexample series of frames. Referring again to FIGS. 1A-1D, it can be seenthat this is because the region position for plot 401 may have heartanatomical structures come into and out of the region during the courseof a cardiac cycle (e.g., in reference also to the corresponding regionlocation shown in FIG. 3, it can be seen that these anatomicalstructures may enter and exit the region location in the lower righthand corner of the region location).

In various embodiments, if the image data in the region position isprimarily black during one phase of the cardiac cycle (either diastoleor systole; e.g., because heart anatomical structures are not present inthe region), the standard deviation in the region may be low becausethere is not much variation amongst the brightness values in thoseframes. However, as the cardiac cycle proceeds to the other phase, aheart anatomical structure may enter the region. This may result in morebright pixels being in the region location, such that there is a broaderdistribution of brightness values. This would result in the standarddeviation of that region location being higher in those frames. Theperiodic cardiac cycle over a series of frames may thus result in theperiodic plot of standard deviation values.

Referring still to FIG. 4, it can be seen that while the periodic motionof cardiac cycles is present on a number of the region locations, theseverity of the changes in the standard deviation value is mostpronounced in plot 407 (labeled as ‘ROI: 7’). This reflects the visualchanges in the image data in the underlying series of frames. Referringsimultaneously to FIG. 3 and FIGS. 1A-1D, the image data for plot 407(the vertically center region location in the horizontally middlecolumn) contains an image of the heart valve during various phases of acardiac cycle. When the heart valve is open, there is generally anabsence of bright pixels in the region location (e.g., as is shown at102 b in FIG. 1B). This results in the region location generallyappearing dark with low variation in image brightness values, such thata low standard deviation value may be associated with these imageframes. In contrast, when the heart valve is closed or about to close,bright structures are generally present in the region location (e.g., asis shown at 102 a, 102 c, 10 d in FIGS. 1A, 1C and 1D respectively).This results in the image data of the region location having generallyboth dark and bright values, such that there is a relatively highstandard deviation value associated with these image frames. Over thesystolic and diastolic phases of a cardiac cycle, the transition from aprimarily dark region to a region with broad variation in brightnessvalues results in the plot 407 shown in FIG. 4, where there is largevariation in the standard deviation values of the image data in theregion location over the example series of frames.

Referring still to FIG. 4, plots 406 (labeled as ‘ROI: 6’) and 412(labeled as ROI: 12’) can be seen exhibiting periodic changes instandard deviation values at a frequency that is similar to that whichis shown in plot 407. Referring again to FIGS. 1A-1D, and FIG. 3, it canbe seen that the region locations corresponding to these plots 406, 412are adjacent (e.g., to the left and immediately below) the horizontallyand vertically center region location containing the heart valveexperiencing the strongest fluctuations in standard deviation values.These adjacent region locations may thus similarly have heart structuresenter/appear within the region location and leave/disappear from theregion location during diastolic and systolic phases of a cardiac cycle,so as to produce similar periodic fluctuations in standard deviationvalues over the series of frames.

In FIG. 4, it can further be seen that plots 408 (labeled as ‘ROI: 8’)and 413 (labeled as ‘ROI: 13’) have generally high standard deviationvalues throughout the example series of frames. Referring simultaneouslyto FIG. 3, these high standard deviation values may be because thecorresponding region locations contain bright structures (e.g., a heartwall). As shown via 104 a-104 d in FIGS. 1A-1D, the heart wall mayexpand and contract over the course of a cardiac cycle. However, sincethe movement of the heart wall remains primarily within each regionlocation (without any bright pixels entering/appearing andexiting/disappearing from the region locations), the corresponding plots408, 413 in FIG. 4 generally fluctuate around a relatively-high standarddeviation value without dipping to lower standard deviation values.

As noted, traditional methods of automating heart rate calculations onultrasound images generally involve automating M-mode operation. Forexample, these systems may attempt to automate the plotting of brightpixels corresponding to heart structures as they move over a sequence offrames. However, even in systems that use a Fourier transform toidentify the frequency of such movement, the bright pixels wouldgenerally need to produce a waveform pattern before the Fouriertransform can be applied to it. Obtaining a waveform pattern that has asufficiently strong signal may be difficult because the axis along whichthe motion of bright pixels traverse may need to be identified. Theidentification of this axis (e.g., the scan line in traditional M-modeoperation) may be difficult and may require re-orientation of theultrasound scanner to so that heart wall motion moves along such line.

In contrast, the present embodiments analyze a statistical calculation(e.g., a standard deviation calculation) over the image data in a givenregion location over a series of frames. Analyzing the statisticalcalculation may provide a more robust algorithm that can monitor changesin the variation of brightness in a region location, without regard toan axis of motion for bright pixels in an image. This may allow theheart rate to be determined more readily whenever an imaged heart iswithin the view of the series of frames, without the repositioning ofthe scanner typically needed in traditional automated heart ratecalculation methods.

Referring back to FIG. 2, at 220, for each region location, the methodmay next involve determining a frequency of change in the statisticalcalculation over the plurality of ultrasound images. As shown in FIG. 4,it can be seen that the plots for the region locations with thepredominant heart structure motion experience fluctuations in thestatistical calculations (e.g., standard deviation values) over a numberof frames. As discussed below in greater detail, this frequency may beused to calculate the heart rate of an imaged heart.

Act 220 may performed in various ways. For example, in some embodiments,one or more peak-to-peak measurements of data in one or more plots 40may be calculated to determine the frequency of change. In some suchembodiments, the peak-to-peak calculations may be selected to beperformed on only the plots exhibiting the greatest fluctuations in thestatistical calculations (e.g., plot 407, as may identified usingsuitable mathematical methods).

Additionally or alternatively, act 220 may involve performing a Fouriertransform on the statistical calculations of the region location acrossthe plurality of ultrasound images (e.g., the statistical calculationsover the ultrasound images would be considered the signal on which theFourier transform is performed). As a person skilled in the art wouldunderstand, performing a Fourier analysis of a given signal may allowdecomposition of that signal into constituent frequencies. A resultantpower spectrum may allow identification of where the energy in thesignal is mostly highly concentrated. As discussed below with respect toFIG. 7, the dominant frequency from the power spectrum can be selectedas the frequency of change for a region location.

To enhance robustness of the present methods, an optional act may beperformed in some embodiments to filter out frequencies that are notlikely to reflect the heart rate of an imaged heart. For example, theheart rate of an adult human is not likely to be below ‘20’ bpm or above‘220’ bpm, and a fetal heart rate is likely being between ‘100’-‘200’bpm. As a result, any frequency not within this range in the signal fora given region location can be filtered out. In various embodiments,this optional step may involve performing band pass filtering. In theexamples discussed below with regards to FIGS. 5 and 6, the band passfiltering is performed before the determining of frequencies of changeusing a Fourier analysis shown in FIG. 7. However, in variousembodiments, it may be possible to perform the band-pass filteringafter, or as a part of, the determining of frequencies of change.

Referring to FIG. 5, shown there generally as 50 is a chart showing aplot of a statistical calculation and a corresponding plot of its bandpass filtered signal over a number of frames, for a given region shownin FIG. 3, in accordance with at least one embodiment of the presentinvention. In FIG. 5, the plot shown in a solid dark line 407corresponds to the plot 407 shown in FIG. 4. As can be seen, there are anumber of high frequency components 550 in the underlying signal thatmay be present (e.g., due to noise and/or speckle). The plot 507 in FIG.5 (shown in dotted line) shows the result of performing a band passfilter on the signal 407 to filter out frequencies that are unlikely toreflect the heart beat of an imaged heart. As can be seen, the highfrequency components 550 of the signal 407 has been filtered out in theplot 507, such that the areas 560 where the plot would contain highfrequency components 550 are smoothed out. In the example plots of FIG.5, the filtered plot 507 is also normalized around a ‘0’ amplitude onthe Y-axis to provide a clear visualization of the filtered plot 507against the original plot 407.

Referring to FIG. 6, shown there generally as 60 are charts showingplots of band pass filtered versions of the statistical calculationsshown in FIG. 4, for each region shown in FIG. 3, in accordance with atleast one embodiment of the present invention. Each of the plots 60 inFIG. 6 correspond to plots in the same row and column position shown inFIG. 4, except that the plots shown in FIG. 6 have been band passfiltered, and normalized around a ‘0’ amplitude on the Y-axis.

Referring simultaneously to FIGS. 4 and 6, it can be seen that a numberof the frequencies that are too low or too high to represent heart rateshave been filtered out in the plots 60 of FIG. 6. For example, whencomparing plots 606, 601, 612 in FIG. 6 to their counterpart plots 406,401, 412 in FIG. 4, it can be seen that a number of the higher frequencycomponents have been filtered out—in a manner similar to plot 507 and407 discussed above in FIG. 5 (plot 507 is also viewable in thevertically center plot in the middle column of FIG. 6). Similarly, whencomparing plots 608, 613, 614 in FIG. 6 to plots 408, 413, 414 in FIG.4, it can be seen that the lower frequency components 450, 470, 460present in the plots 408, 413, 414 of FIG. 4 are no longer present inthe corresponding areas 650, 670, 660 in the plots 608, 613, 614 of FIG.6.

As the lower frequency components may be due to the initial movement ofthe scanner that positions the imaged heart into the field of view,filtering out the lower frequencies in this optional step, while notrequired, may facilitate ease of identifying the dominant determinedfrequency in subsequent acts. Similarly, filtering out the highfrequencies present in the original plots 40 of FIG. 4 may alsofacilitate ease of identifying the dominant determined frequency insubsequent acts because high frequency signals present in the plots 40of FIG. 4 may represent noise and/or speckle.

Referring back to FIG. 2, after act 220 and an optional act ofperforming a band pass filter (not shown in FIG. 2), the method mayproceed to determine the region location having a dominant determinedfrequency of change in the statistical calculation over the plurality ofultrasound images (act 225). For example, in embodiments where a Fouriertransform is performed to identify the dominant frequency, this mayinvolve selecting the region location, of the plurality of regions,having the highest magnitude for the frequency of change.

Referring to FIG. 7, shown there generally as 70 are plots offrequencies present on the band pass filtered versions of thestatistical calculations shown in FIG. 6, for each region shown in FIG.3, in accordance with at least one embodiment of the present invention.Each of the plots 70 correspond to plots in the same row and columnposition shown in FIGS. 6 and 4. However, plots 70 shown in FIG. 7 showthe magnitudes resulting from a Fourier analysis (e.g., a DiscreteFourier Transform (DFT)) on the plots 60 shown in FIG. 6.

In FIG. 7, it can be seen that of all the plots 70, plot 707 (for ‘ROI:7’) has the highest magnitude value at a frequency 750 around ‘80’ onthe X-axis. In the example embodiment, the region location correspondingto plot 707 can thus be selected as the region location having thehighest magnitude for the frequency of change.

It can also be seen that in FIG. 7, there are a number of differentplots also having peaks at a substantially similar frequency (e.g., near‘80’ on the X-axis). For example, this common peak frequency can be seenat least in plots 701, 706, 712, 713, 708, and 703, where the peakfrequencies are variously highlighted as 750 a-750 f. As discussedabove, these various plots having a similar peak frequency may be due tothe region locations corresponding to these plots experiencing similarfluctuations in the statistical calculation (but to a lesser degree) asthe region location (e.g., ‘ROI: 7’) with the highest such fluctuations.

While various plots 70 in FIG. 7 may have similar peak frequencies (andthus may potentially also be used to identify the frequency forcalculating the heart rate of the imaged heart), selecting the plot andregion location (e.g., plot 707 in FIG. 7) with the highest magnitudefor the peak frequency may enhance confidence that the frequencyultimately selected for calculating the heart rate of the imaged heartis accurate.

For example, while in the example series of frames analyzed in FIG. 7,there are other plots that also have substantially similar peakfrequencies, the difference between the magnitude values at differentfrequencies in these plots are generally less pronounced. Depending onthe underlying series of ultrasound image frames, it is possible thatthe peak frequency in these other plots may not in fact reflect theheart rate of an imaged heart. For example, because the differencebetween the magnitude values at different frequencies is smaller, thefrequency representative of the heart rate may potentially be dominatedby another frequency (e.g., from noise or other structural movementscaptured in the images). Selecting the peak frequency from one of theseother plots may therefore potentially result in a less accurate (e.g.,potentially mistaken) determination of the frequency corresponding tothe heart rate.

In contrast, by selecting the region location with the highestmagnitude, the present methods can focus on the region location havingthe most pronounced/severe periodic motion within the image. Even ifother region locations have other types of periodic motion with lesserseverity, it is the most pronounced periodic motion that is likely toreflect the heart rate of an imaged heart over a series of ultrasoundframes. In the present embodiments, the selection of the region locationwith the highest magnitude for the peak frequency may thus allow formore robust operation; e.g., the methods can be used on ultrasound framesequences containing not only an imaged heart but also other incidentalstructures with motion (which would be dismissed when determining theheart rate).

The plots 70 of FIG. 7 show the result of a Fourier analysis on the bandpass filtered plots of FIG. 6. However, in various embodiments, it ispossible to perform the Fourier transform on the original plots of thestatistical calculation that have not had the optional step of band passfiltering performed (e.g., the plots 40 shown in FIG. 4 in the exampleseries of frames discussed herein). For example, in some embodiments,the methods discussed herein may be robust enough to not require theoptional band-pass filtering act. This is because selection of theregion location with the highest magnitude for the frequency of changein act 225 of FIG. 2 can avoid mistaken selection of frequencies that donot correspond to the periodic motion of the heart rate, since thoseother non-heart-rate frequencies may generally be less pronounced whencompared to the dominant periodic motion of a heart beating.

Referring back to FIG. 2, the method may next proceed to act 230 toidentify the selected region location having the dominant determinedfrequency as a region of interest (ROI). After having identified theROI, the method may proceed to act 235 and calculate the heart ratebased on the determined frequency of change of the ROI. In someembodiments, this calculating may involve translating the dominantdetermined frequency of change from a per-frame basis to a per-unit-timebasis. For example, this translating may be performed based on a framerate of the ultrasound image feed.

As illustrated in FIG. 7, the values along the X-axis are shown ashaving already been calculated to be in units of beats per minute (bpm),such that the peak frequency at ‘80’ for the region location 707 havingthe highest magnitude in the Fourier analysis corresponds to a heartrate of ‘80’ bpm. However, in some embodiments, the units of the plot ofthe Fourier analysis may instead correspond to the number of framesanalyzed. In these embodiments, the peak for the Fourier analysis may beidentified at a given frame along the sequence of frames. To calculate ameaningful heart rate in these embodiments, the peak value at this framenumber may be divided by the length of frames used for the Fourieranalysis (e.g., a Fast Fourier Transform (FFT) length—as will beapparent to persons skilled in the art, this is a parameter that is theclosest power of ‘2’ to the number of frames in the ultrasound sequenceof frame being analyzed). The result can then be multiplied by thesampling rate (typically this is the frame rate, in units of frames persecond (fps)). The result may then be multiplied by ‘60’ (seconds perminute) to arrive at a meaningful bpm heart rate.

Modifications to the methods discussed herein may be possible in variousembodiments. For example, as discussed above, the region location withthe highest magnitude is selected as the region location from which toidentify the dominant frequency for calculating heart rate. However, inalternative embodiments, the frequency for calculating the heart ratemay be identified in a different manner. As noted with respect to FIG.7, a number of different region locations can have substantially thesame peak frequency. Thus, in some alternative embodiments, theprevalence of a peak frequency can be used as a way for identifying thefrequency for calculating the heart rate. For example, this may involveidentifying the most common peak frequency amongst the different regionlocations, and using it as the dominant frequency for calculating theheart rate. These embodiments may, for example, be suitable whendetermining the heart rate of an adult heart (e.g., as compared to afetal heart), where periodic motion for a cardiac cycle may be presentin multiple region locations.

Additionally or alternatively, an average magnitude spectrum can becalculated by using region locations where the magnitude of the spectrumis higher than a certain threshold. The peak frequency may then beselected from the average magnitude spectrum; either as an independentdetermination of the dominant frequency or as a confirmation of adominant frequency calculated in a different manner.

In another example modification, prior to the dividing the plurality ofultrasound images into regions (act 210 in FIG. 2), one or more of theplurality of the images may be scaled down. Scaling down the images maymake the various image processing acts subsequent to act 210 moreefficient, and reduce the computational resources required to performthe present methods. For example, this may better facilitate theperformance of the present methods on ultrasound scanners with limitedreal-time processing capabilities, such as on handheld wirelessultrasound scanners.

Referring to FIG. 8, shown there generally as 800 is a functional blockdiagram of an ultrasound system, in accordance with at least oneembodiment of the present invention. For example, the ultrasound imagingsystem 800 (or any individual apparatus or device included therein) maybe configured to perform the method of FIG. 2 to determine a heart rateof an imaged heart in an ultrasound image feed.

Ultrasound imaging system 800 may include a number of differentapparatus or computing devices. As illustrated, ultrasound imagingsystem 800 includes an ultrasound acquisition unit 804 configured totransmit ultrasound energy to a target object, receive ultrasound energyreflected from the target object, and generate ultrasound image databased on the reflected ultrasound energy. The ultrasound acquisitionunit 804 may include a transducer 826 which converts electric currentinto ultrasound energy and vice versa. Transducer 826 may transmitultrasound energy to the target object which echoes off the tissue. Theechoes may be detected by a sensor in transducer 826 and relayed througha bus 832 to a processor 836. Processor 836 may interpret and processthe echoes to generate image data of the scanned tissue. In someembodiments, the ultrasound acquisition unit 804 (or various componentsthereof) may be provided as a handheld ultrasound probe or scanner thatis in communication with other components of the ultrasound imagingsystem 800. For example, the handheld probe may include the transducer826 of ultrasound acquisition unit 804. Ultrasound acquisition unit 804may also include storage device 828 (coupled to and accessible by bus832) for storing software or firmware instructions, configurationsettings (e.g., sequence tables), and/or ultrasound image data.

Although not illustrated, as will be apparent to one of skill in theart, the ultrasound imaging system 800 may include other components foracquiring, processing and/or displaying ultrasound image data. Theseinclude, but are not limited to: a scan generator, transmit beamformer,pulse generator, amplifier, analogue to digital converter (ADC), receivebeamformer, signal processor, data compressor, wireless transceiverand/or image processor. Each of these may be components of ultrasoundacquisition unit 804 and/or electronic display unit 802 (describedbelow).

Ultrasound imaging system 800 may include an electronic display unit 802which is in communication with ultrasound acquisition unit 804 viacommunication interfaces 822/834. In various embodiments, communicationinterfaces 822/834 may allow for wired or wireless connectivity (e.g.,via Wi-Fi™ and/or Bluetooth™) between the electronic display unit 802and the ultrasound acquisition unit 804. Electronic display unit 802 maywork in conjunction with ultrasound acquisition unit 804 to control theoperation of ultrasound acquisition unit 804 and display the imagesacquired by the ultrasound acquisition unit 804. An ultrasound operatormay interact with the user interface provided by display unit 802 tosend control commands to the ultrasound acquisition unit 804 (e.g., toinitiate an operation mode that initiates execution of the methods fordetermining a heart rate described herein). The electronic display unit802 may be a portable device, which may include a mobile computingdevice (e.g. smartphone), tablet, laptop, or other suitable deviceincorporating a display and a processor and capable of accepting inputfrom a user and processing and relaying the input to control theoperation of the ultrasound acquisition unit 804 as described herein.

Each of ultrasound acquisition unit 804 and display unit 802 may haveone or more input components 824, 806 and/or one or more outputcomponents 830, 812. In the FIG. 8 embodiment, ultrasound acquisitionunit 804 may include an input component 824 which is configured toaccept input from the user (e.g., to turn on the ultrasound acquisitionunit 804 or control the connection of the ultrasound acquisition unit804 to the electronic display unit 802). For example, in someembodiments, ultrasound acquisition unit 804 may also include an outputcomponent 830, such as a LED indicator light which can output the statusof the ultrasound acquisition unit 804.

In the FIG. 8 embodiment, display unit 802 may include an inputcomponent 806 configured to accept input from the user. Certain inputreceived at input component 806 may be relayed to ultrasound acquisitionunit 804 to control the operation of ultrasound acquisition unit 804.Display unit 802 may also include an output component 812, such as adisplay screen, which displays images based on image data acquired byultrasound acquisition unit 804. In particular embodiments, display unit802′s input component 806 may include a touch interface layered on topof the display screen of the output component 812. Electronic displayunit 802 may also include memory 808, Random Access Memory (RAM) 814,Read Only Memory (ROM) 810, and persistent storage device 816, which mayall be connected to bus 818 to allow for communication therebetween andwith processor 820. Ultrasound acquisition unit 804 may contain memory(e.g., storage device 828) that may be accessible by processor 836. Anynumber of these memory elements may store software or firmware that maybe accessed and executed by processor 820 and/or processor 836 to, inpart or in whole, perform the acts of the methods described herein(e.g., so that the processor 820 and/or processor 836 is configured toperform the methods described herein to determine a heart rate of animaged heart in an ultrasound image feed).

In some embodiments, all of the input controls and display screennecessary for the operation of the ultrasound imaging system 800 may beprovided by input and output components 806, 812 of the display unit802. In such cases input and output components 824, 830 of ultrasoundacquisition unit 804 may be optional and/or omitted. In certainembodiments, the ultrasound acquisition unit 804 may be a handheld probe(i.e. including transducer 826) which is in communication with thedisplay unit 802 over the communications interfaces 822/834 tofacilitate operation of the ultrasound acquisition unit 804 andprocessing and display of ultrasound images.

In various embodiments, at least a portion of the processing of theimage data corresponding to the reflected ultrasound energy detected bythe handheld probe's transducer 826 may be performed by one or more ofprocessors internal to the ultrasound acquisition unit 804 (such as bythe processor 836) and/or by processors external to the ultrasoundacquisition unit 804 (such as the processor 820 of electronic displayunit 802). By having some of the image data processing tasks typicallyperformed by a processor 836 of ultrasound acquisition unit 804 beperformed instead by a processor 820 of the display unit 802, lessphysical processing hardware may need to be provided on the ultrasoundacquisition unit 804. This may facilitate a lightweight, portable designand construction for the ultrasound acquisition unit 804 (e.g., when itis a handheld probe). In particular embodiments the handheld probe mayhave a mass that is less than approximately 1 kg (2 lbs).

In some embodiments, the output component 830 of ultrasound acquisitionunit 804 may include a display screen, which can be configured todisplay or otherwise output the images acquired by ultrasoundacquisition unit 804 (in addition to or alternative to displaying suchimages on the display unit 802).

As noted, the ultrasound imaging system 800 of FIG. 8 may be configuredto perform the method of FIG. 2, so as to determine a heart rate of animaged heart in an ultrasound image feed. Steps of method 200 in FIG. 2may be implemented as software or firmware contained in: a programmemory 808, 814, 810 or storage device 816 accessible to a processor 820of display unit 802, and/or a storage device 828 accessible to processor836 of ultrasound acquisition unit 804. Processor 820/836 mayindependently or collectively implement various acts of method 200 ofFIG. 2 by executing software instructions provided by the software.

Scan conversion is a process that converts image data to allow it to bedisplayed in a form that is more suitable for human visual consumption.For example, this may involve converting the image data from the dataspace (e.g. polar coordinate form) to the display space (e.g. Cartesiancoordinate form). Depending on the location of where the methods of thepresent embodiments are performed, the ultrasound images on which themethods are performed may differ. For example, if the methods describedherein are performed by processor 836 on the ultrasound acquisition unit804, the ultrasound images on which the methods may be performed may bepre-scan-converted data. Additionally or alternatively, if the methodsdescribed herein are performed by processor 820 on the display unit 802,the methods described herein may be performed on post-scan-converteddata.

Referring to FIG. 9, shown there generally as 900 is a method ofdetermining a heart rate of an imaged in an ultrasound image feed, inaccordance with at least one embodiment of the present invention. Asdiscussed below, some acts of method 900 in FIG. 9 are analogous to actsdiscussed above with respect to FIG. 2.

At 905, a plurality of ultrasound images may be acquired. This act maybe performed like act 205 in FIG. 2 discussed above.

At 910, a difference between at least two successive images in theplurality of ultrasound images may be calculated to determine pixeldisplacement of one or more pixel locations. This may involve analyzingthe values of some or all X, Y pixel locations across multiple images inthe plurality of ultrasound images, to determine how structures/objectswithin the images experience motion over the course of the ultrasoundimage feed. For example, in the calculated difference, pixel locationswhere there is no motion will generally be zero or black. However, pixellocations with motion will generally appear to have some values. Oncethe X, Y locations experiencing motion are identified, the method mayproceed to act 915.

At 915 (shown in dotted outline), an optional morphological operationmay be performed to locate a region of interest (ROI) in the imageswhere there is periodic motion. In various embodiments, themorphological operation may be any suitable mathematical morphologyoperation that assists in localizing the motion to a common region onthe ultrasound images. Various example morphological operations that maybe used include erosion, dilation, opening, and closing. As theidentified X,Y positions having motion from act 210 may be at variouslocations on the images, performing a morphological operation may helpto localize and consolidate the various X,Y positions into a commonregion for the purpose of identifying an ROI.

Act 915 is optional and may be omitted. However, since simplycalculating the difference between two successive frames in act 910 maypotentially result in a large number of X,Y pixel positions experiencingmotion, performing the optional morphological operation at act 915 canallow identification of pixel positions that experience relatively largeamounts of motion. For example, a morphological operation (e.g., todilate or open X,Y positions experiencing motion) may consolidate X,Ypositions that are proximate to each other, so as to identify an ROI. Inthis manner, the morphological operation may serve to filter out thepixel locations experiencing less motion.

In various embodiments, the method of FIG. 9 may be performed onultrasound image feeds containing imaged fetal hearts. Since fetal heartstructures are relatively smaller, the motion of the fetal heart istypically localized to a given region of the ultrasound image feed. Themethod of FIG. 9 can then identify the large motion of the fetal heart,and filter out the lesser motion of other imaged areas.

At 920, for the identified ROI, a statistical calculation on the imagedata of the region location may be performed over the plurality of theultrasound images. This act may be performed like the performance of astatistical calculation discussed above in relation to act 215 of FIG.2. However, whereas act 215 in FIG. 2 involves performing thestatistical calculation on multiple region locations, act 920 involvesperforming the statistical calculation on the ROI identified at act 915(if the optional act is performed) or an ROI determined from act 910.

At 925, a frequency of change in the statistical calculation at the ROIover the plurality of ultrasound images can be determined. This act maybe performed like act 220 in FIG. 2 is performed for a given regionlocation (e.g., on a plot of the statistical calculation values acrossthe plurality of ultrasound images).

At 930, the method may involve determining the dominant determinedfrequency of change in the statistical calculation at the ROI over theplurality of ultrasound images. This act may be performed like act 225in FIG. 2 is performed for a given region location. For example, aFourier analysis may be performed on a plot of the statisticalcalculation (e.g., a standard deviation calculation) over the imageframes. A dominant frequency may then be identified from the frequencycorresponding to the highest (e.g., peak) magnitude of the Fourieranalysis.

At 935, the heart rate may be calculated based on the determineddominant frequency of change of the ROI. This act may be performed likeact 235 discussed above with respect to FIG. 2.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize that may be certainmodifications, permutations, additions and sub-combinations thereof.While the above description contains many details of exampleembodiments, these should not be construed as essential limitations onthe scope of any embodiment. Many other ramifications and variations arepossible within the teachings of the various embodiments.

INTERPRETATION OF TERMS

Unless the context clearly requires otherwise, throughout thedescription and the claims:

-   -   “comprise”, “comprising”, and the like are to be construed in an        inclusive sense, as opposed to an exclusive or exhaustive sense;        that is to say, in the sense of “including, but not limited to”;    -   “connected”, “coupled”, or any variant thereof, means any        connection or coupling, either direct or indirect, between two        or more elements; the coupling or connection between the        elements can be physical, logical, or a combination thereof;    -   “herein”, “above”, “below”, and words of similar import, when        used to describe this specification, shall refer to this        specification as a whole, and not to any particular portions of        this specification;    -   “or”, in reference to a list of two or more items, covers all of        the following interpretations of the word: any of the items in        the list, all of the items in the list, and any combination of        the items in the list;    -   the singular forms “a”, “an”, and “the” also include the meaning        of any appropriate plural forms.

Unless the context clearly requires otherwise, throughout thedescription and the claims:

Words that indicate directions such as “vertical”, “transverse”,“horizontal”, “upward”, “downward”, “forward”, “backward”, “inward”,“outward”, “vertical”, “transverse”, “left”, “right”, “front”, “back”,“top”, “bottom”, “below”, “above”, “under”, and the like, used in thisdescription and any accompanying claims (where present), depend on thespecific orientation of the apparatus described and illustrated. Thesubject matter described herein may assume various alternativeorientations. Accordingly, these directional terms are not strictlydefined and should not be interpreted narrowly.

Embodiments of the invention may be implemented using specificallydesigned hardware, configurable hardware, programmable data processorsconfigured by the provision of software (which may optionally comprise“firmware”) capable of executing on the data processors, special purposecomputers or data processors that are specifically programmed,configured, or constructed to perform one or more steps in a method asexplained in detail herein and/or combinations of two or more of these.Examples of specifically designed hardware are: logic circuits,application-specific integrated circuits (“ASICs”), large scaleintegrated circuits (“LSIs”), very large scale integrated circuits(“VLSIs”), and the like. Examples of configurable hardware are: one ormore programmable logic devices such as programmable array logic(“PALs”), programmable logic arrays (“PLAs”), and field programmablegate arrays (“FPGAs”). Examples of programmable data processors are:microprocessors, digital signal processors (“DSPs”), embeddedprocessors, graphics processors, math co-processors, general purposecomputers, server computers, cloud computers, mainframe computers,computer workstations, and the like. For example, one or more dataprocessors in a control circuit for a device may implement methods asdescribed herein by executing software instructions in a program memoryaccessible to the processors.

For example, while processes or blocks are presented in a given orderherein, alternative examples may perform routines having steps, oremploy systems having blocks, in a different order, and some processesor blocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or subcombinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times.

The invention may also be provided in the form of a program product. Theprogram product may comprise any non-transitory medium which carries aset of computer-readable instructions which, when executed by a dataprocessor (e.g., in a controller and/or ultrasound processor in anultrasound machine), cause the data processor to execute a method of theinvention. Program products according to the invention may be in any ofa wide variety of forms. The program product may comprise, for example,non-transitory media such as magnetic data storage media includingfloppy diskettes, hard disk drives, optical data storage media includingCD ROMs, DVDs, electronic data storage media including ROMs, flash RAM,EPROMs, hardwired or preprogrammed chips (e.g., EEPROM semiconductorchips), nanotechnology memory, or the like. The computer-readablesignals on the program product may optionally be compressed orencrypted.

Where a component (e.g. a software module, processor, assembly, device,circuit, etc.) is referred to above, unless otherwise indicated,reference to that component (including a reference to a “means”) shouldbe interpreted as including as equivalents of that component anycomponent which performs the function of the described component (i.e.,that is functionally equivalent), including components which are notstructurally equivalent to the disclosed structure which performs thefunction in the illustrated exemplary embodiments of the invention.

Specific examples of systems, methods and apparatus have been describedherein for purposes of illustration. These are only examples. Thetechnology provided herein can be applied to systems other than theexample systems described above. Many alterations, modifications,additions, omissions, and permutations are possible within the practiceof this invention. This invention includes variations on describedembodiments that would be apparent to the skilled addressee, includingvariations obtained by: replacing features, elements and/or acts withequivalent features, elements and/or acts; mixing and matching offeatures, elements and/or acts from different embodiments; combiningfeatures, elements and/or acts from embodiments as described herein withfeatures, elements and/or acts of other technology; and/or omittingcombining features, elements and/or acts from described embodiments.

It is therefore intended that the following appended claims and claimshereafter introduced are interpreted to include all such modifications,permutations, additions, omissions, and sub-combinations as mayreasonably be inferred. The scope of the claims should not be limited bythe preferred embodiments set forth in the examples, but should be giventhe broadest interpretation consistent with the description as a whole.

What is claimed is:
 1. A method of determining a heart rate of an imagedheart in an ultrasound image feed, the method comprising: acquiring aplurality of ultrasound images; for each ultrasound image of theplurality of ultrasound images, dividing the image into a plurality ofregions, wherein each region is positioned so that the regioncorresponds to a substantially similar region location present acrossthe plurality of ultrasound images; for each region location on each ofthe plurality of ultrasound images, performing a statistical calculationon image data of the region location; for each region location,determining a frequency of change in the statistical calculation overthe plurality of ultrasound images; determining the region locationhaving a dominant determined frequency of change in the statisticalcalculation over the plurality of ultrasound images; identifying aselected region location having the dominant determined frequency as aregion of interest (ROI); and calculating the heart rate based on thedetermined frequency of change of the ROI.
 2. The method of claim 1,wherein the statistical calculation comprises an indication of variationin brightness of the image data of the region location.
 3. The method ofclaim 2, wherein the statistical calculation comprises a standarddeviation of brightness values of the image data of the region location.4. The method of claim 1, wherein when determining the frequency ofchange for each region location over the plurality of ultrasound images,the method further comprises: performing a Fourier transform on thestatistical calculations of the region location across the plurality ofultrasound images, to generate a power spectrum; and selecting a peakfrequency from the power spectrum as the frequency of change for theregion location.
 5. The method of claim 1, wherein when determining theregion location having the dominant determined frequency of change inthe statistical calculation, the method further comprises: selecting theregion location, of the plurality of regions, having the highestmagnitude for the frequency of change.
 6. The method of claim 1, furthercomprising: prior to the dividing, scaling down at least one ultrasoundimage of the plurality of ultrasound images.
 7. The method of claim 1,wherein the plurality of regions forms a grid.
 8. The method of claim 1,wherein a total number of region locations is less than
 50. 9. Themethod of claim 1, wherein a total number of region locations is atleast
 2. 10. The method of claim 1, further comprising: performing aband pass filter on at least one determined frequency of a regionlocation to eliminate non-heart-rate frequencies.
 11. The method ofclaim 1, wherein the calculating the heart rate comprises: translatingthe dominant determined frequency of change from a per-frame basis to aper-unit-time basis, and wherein the translating is performed based on aframe rate of the ultrasound image feed.
 12. An ultrasound imagingapparatus for determining a heart rate of an imaged heart in anultrasound image feed, the apparatus comprising: a processor; and amemory storing instructions for execution by the processor, wherein whenthe instructions are executed by the processor, the processor isconfigured to: acquire a plurality of ultrasound images; for eachultrasound image of the plurality of ultrasound images, divide the imageinto a plurality of regions, wherein each region is positioned so thatthe region corresponds to a substantially similar region locationpresent across the plurality of ultrasound images; for each regionlocation on each of the plurality of ultrasound images, perform astatistical calculation on image data of the region location; for eachregion location, determine a frequency of change in the statisticalcalculation over the plurality of ultrasound images; determine theregion location having a dominant determined frequency of change in thestatistical calculation over the plurality of ultrasound images;identify a selected region location having the dominant determinedfrequency as a region of interest (ROI); and calculate the heart ratebased on the determined frequency of change of the ROI.
 13. Theultrasound imaging apparatus of claim 12, wherein the statisticalcalculation comprises a standard deviation of brightness values of theimage data of the region location.
 14. The ultrasound imaging apparatusof claim 12, wherein when determining the frequency of change for eachregion location over the plurality of ultrasound images, the processoris further configured to: perform a Fourier transform on the statisticalcalculations of the region location across the plurality of ultrasoundimages, to generate a power spectrum; and select a peak frequency fromthe power spectrum as the frequency of change for the region location.15. The ultrasound imaging apparatus of claim 12, wherein whendetermining the region location having the dominant determined frequencyof change in the statistical calculation, the processor is furtherconfigured to: select the region location, of the plurality of regions,having the highest magnitude for the frequency of change.
 16. Theultrasound imaging apparatus of claim 12, wherein the processor isfurther configured to: prior to the dividing, scale down at least oneultrasound image of the plurality of ultrasound images.
 17. Theultrasound imaging apparatus of claim 12, wherein the processor isfurther configured to: perform a band pass filter on at least onedetermined frequency of a region location to eliminate non-heart-ratefrequencies.
 18. A computer readable medium storing instructions fordetermining a heart rate of an imaged heart in an ultrasound image feed,the instructions for execution by a processor of a computing device,wherein when the instructions are executed by the processor, theprocessor is configured to: acquire a plurality of ultrasound images;for each ultrasound image of the plurality of ultrasound images, dividethe image into a plurality of regions, wherein each region is positionedso that the region corresponds to a substantially similar regionlocation present across the plurality of ultrasound images; for eachregion location on each of the plurality of ultrasound images, perform astatistical calculation on image data of the region location; for eachregion location, determine a frequency of change in the statisticalcalculation over the plurality of ultrasound images; determine theregion location having a dominant determined frequency of change in thestatistical calculation over the plurality of ultrasound images;identify a selected region location having the dominant determinedfrequency as a region of interest (ROI); and calculate the heart ratebased on the determined frequency of change of the ROI.
 19. The computerreadable medium of claim 18, wherein the statistical calculationcomprises a standard deviation of brightness values of the image data ofthe region location.
 20. The computer readable medium of claim 18,wherein when determining the frequency of change for each regionlocation over the plurality of ultrasound images, the instructionsfurther configure the processor to: perform a Fourier transform on thestatistical calculations of the region location across the plurality ofultrasound images, to generate a power spectrum; and select a peakfrequency from the power spectrum as the frequency of change for theregion location.